In this example, I would use the P-values samples from the MultiPy package. With this package, we would test various methods I have explained above. Let’s get started by installing the necessary package. Luckily, there is a package for Multiple Hypothesis Correction called MultiPy that we could use. I hope you already understand the basic concept of Multiple Hypothesis Correction because, in these parts, I would show you the easier parts Using Python Package to do our Multiple Hypothesis Correction.

Multiple Hypothesis Correction using MultiPy What’s the probability of one significant result just due to chance? However, remember you have 20 hypotheses to test against your target with a significance level of 0.05. This is feasible and seems like a good idea. You might think to test each feature using hypothesis testing separately with some level of significance α 0.05. Often case that we use hypothesis testing to select which features are useful for our prediction model for example, there are 20 features you are interested in as independent (predictor) features to create your machine learning model. If the p-value is significant, we can reject the null hypothesis and claim that the findings support the alternative hypothesis. In the hypothesis testing, we test the hypothesis against our chosen 𝛼 level or p-value (often, it is 0.05).

Null Hypothesis (H0): There is no relationship between the variablesĪlternative Hypothesis (H1): There is a relationship between variables The hypothesis could be anything, but the most common one is the one I presented below. There is always a minimum of two different hypotheses Null Hypothesis and Alternative Hypothesis.

Sigmaplot 11 do not test bonferroni